Dataset Information

Compromised word-level neural tracking in the high-gamma band for children with attention deficit hyperactivity disorder.

ABSTRACT: Children with attention deficit hyperactivity disorder (ADHD) exhibit pervasive difficulties in speech perception. Given that speech processing involves both acoustic and linguistic stages, it remains unclear which stage of speech processing is impaired in children with ADHD. To investigate this issue, we measured neural tracking of speech at syllable and word levels using electroencephalography (EEG), and evaluated the relationship between neural responses and ADHD symptoms in 6-8 years old children. Twenty-three children participated in the current study, and their ADHD symptoms were assessed with SNAP-IV questionnaires. In the experiment, the children listened to hierarchical speech sequences in which syllables and words were, respectively, repeated at 2.5 and 1.25 Hz. Using frequency domain analyses, reliable neural tracking of syllables and words was observed in both the low-frequency band (<4 Hz) and the high-gamma band (70-160 Hz). However, the neural tracking of words in the high-gamma band showed an anti-correlation with the ADHD symptom scores of the children. These results indicate that ADHD prominently impairs cortical encoding of linguistic information (e.g., words) in speech perception.

SUBMITTER: Luo C

PROVIDER: S-EPMC10196181 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Compromised word-level neural tracking in the high-gamma band for children with attention deficit hyperactivity disorder.

Luo Cheng C Gao Yayue Y Fan Jianing J Liu Yang Y Yu Yonglin Y Zhang Xin X

Frontiers in human neuroscience 20230505

Children with attention deficit hyperactivity disorder (ADHD) exhibit pervasive difficulties in speech perception. Given that speech processing involves both acoustic and linguistic stages, it remains unclear which stage of speech processing is impaired in children with ADHD. To investigate this issue, we measured neural tracking of speech at syllable and word levels using electroencephalography (EEG), and evaluated the relationship between neural responses and ADHD symptoms in 6-8 years old chi ...[more]

PMID: 37213926

Similar Datasets

Project description:ObjectivesLeveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.MethodsFrom the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.ResultsChildren with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.ConclusionOur study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.

Dataset Information

Compromised word-level neural tracking in the high-gamma band for children with attention deficit hyperactivity disorder.

Publications

Compromised word-level neural tracking in the high-gamma band for children with attention deficit hyperactivity disorder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets